Weekly Exercises #3

library(tidyverse)     # for graphing and data cleaning
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(ggthemes)      # for even more plotting themes
library(geofacet)      # for special faceting with US map layout
theme_set(theme_minimal())       # My favorite ggplot() theme :)

# Lisa's garden data
data("garden_harvest")

# Seeds/plants (and other garden supply) costs
data("garden_spending")

# Planting dates and locations
data("garden_planting")

# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')

Setting up on GitHub!

Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 3rd weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises with garden data

These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

1. Summarize the garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.

garden_harvest %>% 
  mutate(day = wday(date, label = TRUE)) %>% 
  group_by(vegetable, day) %>% 
  summarize(total_wt = sum(weight)) %>% 
  pivot_wider(names_from = day,
              values_from = total_wt)

2. Summarize the garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?

garden_harvest %>% 
  group_by(vegetable, variety) %>% 
  summarize(tot_harvest_lb = weight*0.0022) %>% 
  left_join(plant_date_loc,
            by = c("vegetable", "variety"))

## Error in is.data.frame(y): object 'plant_date_loc' not found

The problem with this code is that not every vegetable in the garden_harvest data set has fully completed data. This means that some data has NA values. We could get rid of these NA values by using an inner join function.

3. I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.

In order to see how much you saved, you could combine both data sets and then sort each vegetable and variety in the garden_harvest. From there we can do an inner join by vegetable and variety which would show us price of what it would cost for each vegetable planted.

4. Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.

garden_harvest %>% 
  filter(vegetable == "tomatoes") %>%
  mutate(variety = fct_reorder(variety, date, min)) %>% 
  group_by(variety) %>%
  summarize(total_harvest_lb = sum(weight*0.0022),
            min_date = min(date)) %>% 
  ggplot(aes(x = total_harvest_lb, y = fct_rev(variety))) +
  geom_col(fill = "tomato2")+
  labs(title = "Tomato Varieties Harvest Weight 
       In Order of Earliest to Latest First Harvest Date",
       y = "",
       x = "Total LBS")

5. In the garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().

garden_harvest %>% 
  mutate(lowercase = str_to_lower(variety),
         length = str_length(variety)) %>%
  group_by(vegetable, variety) %>% 
  summarize(length = mean(length)) %>%
  arrange(vegetable, length)

6. In the garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().

garden_harvest %>% 
  mutate(name_er_ar = str_detect(variety, "er|ar")) %>%
  filter(name_er_ar == TRUE) %>% 
  distinct(vegetable, variety)

Bicycle-Use Patterns

In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.

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Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data-Small.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.

Temporal patterns

It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:

7. A density plot, which is a smoothed out histogram, of the events versus sdate. Use geom_density().

Trips %>% 
  ggplot(aes(x = sdate))+
  geom_density()+
  labs(title = "Distribution of bike rentals by date",
       x = "",
       y = "")

The density plot above shows the distribution of bike rentals over three months. We can see from the plot that earlier months such as October and November have more bike rentals in comparison to December. This may be attributed to the whether and temperate, as less people want to go biking the colder the weather gets.

8. A density plot of the events versus time of day. You can use mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.

Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time = (hour + (minute/60))) %>% 
  ggplot(aes(x = time))+
  geom_density()+
  labs(title = "Distribution of bike rentals by time of day",
      y = "",
       x = "Hours in military time")

The density plot above shows at what time during the day do most people go biking. We can see that people are most likely to go biking mid morning around hours 8-9 and between hours 17-19 after diner time. This bimodal distribution makes sense because its when most people have free time, either before work or after work.

9. A bar graph of the events versus day of the week. Put day on the y-axis.

Trips %>% 
  mutate(wday = wday(sdate, label = TRUE)) %>% 
  ggplot(aes(y = fct_rev(wday)))+
  geom_bar(fill = "darkblue")+
  labs(title = "Number of bike rentals by day of the week",
       x = "",
       y = "")

The barplot above shows the number of bike rentals by day of the week. We can see that on average more people go biking during the week rather than on weekends.

10. Facet your graph from exercise 8. by day of the week. Is there a pattern?

Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time = (hour + (minute/60)),
         wday = wday(sdate, label = TRUE)) %>%
  ggplot(aes(x = time))+
  facet_wrap(vars(wday))+
  geom_density()+
   labs(title = "Distribution of bike rentals by time of day",
       x = "Hours in military time",
       y = "")

From these distribution graphs we can see that that there is a pattern. On weekends midday is the most popular time to rent a bike and this is because people usually don’t work on weekends. As for weekdays there is the bimodal distribution similar to the distribution graph on question eight. This makes sense because people most often have free time before and after work, and prefer to bike when it is light out, therfore giving us the two popular times to bikw which is mid morning and after dinner in the evenings.

The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.

11. Change the graph from exercise 10 to set the fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.

Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time = (hour + (minute/60)),
         wday = wday(sdate, label = TRUE)) %>%
  ggplot(aes(x = time, fill = client))+
  facet_wrap(vars(wday))+
  geom_density(alpha = .5, color = NA)+
   labs(title = "Distribution of bike rentals by time of day
        and client type",
       x = "",
       y = "")

From this distribution we can see that on average, casual riders outnumber registered clients on the weekend. We can also see that during the week that the distribution between casual and registered bikers is different. The casual riders have a normal distribution while the registered riders have a bimodal distribution.

12. Change the previous graph by adding the argument position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?

Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time = (hour + (minute/60)),
         wday = wday(sdate, label = TRUE)) %>%
  ggplot(aes(x = time, fill = client))+
  facet_wrap(vars(wday))+
  geom_density(alpha = .5, color = NA, position = position_stack())+
   labs(title = "Distribution of bike rentals by time of day
        and client type",
       x = "",
       y = "")

This graph tells a much better story than 11. There is not as much overlap which makes it easier for us to interpret the graph. This allows us be certain when making conclusions.

13. In this graph, go back to using the regular density plot (without position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.

Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time = (hour + (minute/60)),
         week_type = wday(sdate, label = TRUE),
         weekend = ifelse(wday(sdate) %in% c(1,7), "weekend", "weekday")) %>% 
  ggplot(aes(x = time, fill = client))+
  facet_wrap(vars(weekend))+
  geom_density(alpha = .5, color = NA)+
   labs(title = "Distribution of bike rentals by time of day, type of day,
        and type of client",
       x = "",
       y = "")

14. Change the graph from the previous problem to facet on client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?

Trips %>% 
  mutate(hour = hour(sdate),
         minute = minute(sdate),
         time = (hour + (minute/60)),
         week_type = wday(sdate, label = TRUE),
         day_type = ifelse(wday(sdate) %in% c(1,7), "weekend", "weekday")) %>% 
  ggplot(aes(x = time, fill = day_type))+
  facet_wrap(vars(client))+
  geom_density(alpha = .5, color = NA)+
   labs(title = "Distribution of Bike Rentals by Time of Day, Type of Day,
        and Type of Client",
       x = "",
       y = "")

I wouldn’t say one graph is better than the other, because they both show us different things. This graph facets on client and fills with weekday rather than it being flipped for the graph for 13. This graph is more helpful if you wanted to look at the distributions within client type.

Spatial patterns

15. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!

Trips %>% 
  count(sstation) %>% 
  inner_join(Stations,
             by = c("sstation" = "name")) %>% 
  ggplot(aes(x = long, y = lat, color = n))+
  geom_point()+
  labs(title = "Total departures from each station
       by latitude and longitude",
       x = "Longitude",
       y = "Latitude")

16. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? (Again, we’ll improve this next week when we learn about maps).

Trips %>% 
  group_by(sstation) %>% 
  summarize(tot_dept = n(), 
            prop_casual = mean(client == "Casual")) %>% 
  left_join(Stations,
            by = c("sstation" = "name")) %>% 
  ggplot(aes(x = long, y = lat, color = prop_casual))+
  geom_point(alpha = 0.5)+
  labs(title = "Stations with a higher percentage
  of departures by casual users",
       x = "Longitude",
       y = "Latitude")

I noticed this graph has a similar distribution to the graph above. We also see clusters around -77.1 longitude to -77.0 longitude. The proportions of most of the points are below 0.5 as well.

Spatiotemporal patterns

17. Make a table with the ten station-date combinations (e.g., 14th & V St., 2014-10-14) with the highest number of departures, sorted from most departures to fewest. Save this to a new dataset and print out the dataset. Hint: as_date(sdate) converts sdate from date-time format to date format.

ten_trip <- Trips %>%
  mutate(sdate = as_date(sdate)) %>% 
  count(sstation, sdate) %>% 
  slice_max(n = 10, order_by = n, with_ties = FALSE)

ten_trip

18. Use a join operation to make a table with only those trips whose departures match those top ten station-date combinations from the previous part.

Trips %>% 
  mutate(sdate = as_date(sdate)) %>% 
  inner_join(ten_trip, 
             by = c("sstation", "sdate"))

19. Build on the code from the previous problem (ie. copy that code below and then %>% into the next step.) and group the trips by client type and day of the week (use the name, not the number). Find the proportion of trips by day within each client type (ie. the proportions for all 7 days within each client type add up to 1). Display your results so day of week is a column and there is a column for each client type. Interpret your results.

DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.

Trips%>% 
  mutate(sdate = as_date(sdate)) %>% 
  inner_join(ten_trip, by = c("sstation", "sdate")) %>%
  mutate(day_of_week = wday(sdate, label = TRUE)) %>% 
  group_by(client, day_of_week) %>% 
  summarize(trips_day = n()) %>% 
  group_by(client) %>% 
  mutate(prop = trips_day/sum(trips_day)) %>% 
  pivot_wider(id_cols = day_of_week,
              names_from = client,
              values_from = prop)

GitHub link

20. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 03_exercises.Rmd, provide a link to the 03_exercises.md file, which is the one that will be most readable on GitHub.

My Weekly 3 Git.

Challenge problem!

This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.

21. In this exercise, you are going to try to replicate the graph below, created by Georgios Karamanis. I’m sure you can find the exact code on GitHub somewhere, but DON’T DO THAT! You will only be graded for putting an effort into this problem. So, give it a try and see how far you can get without doing too much googling. HINT: use facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?